Wildlife monitoring and sampling: when should we consider genetic data in wild populations?

Abstract

Conservation and management of wild populations requires management professionals to devise methods for collecting data that are efficient and accurate. For example, understanding population sizes and stability is important to predicting extinction risk but the lack of comparisons of costs and effectiveness of different methods limits efficient assessment of wild populations. Similarly, monitoring and tracking the spread of wildlife diseases is important for conservation and management, however disease samples and other data have to be collected efficiently and in ways that maximize our understanding of the disease-wildlife systems. In my thesis, we addressed these research gaps in three studies. First, we conducted a meta-analysis of peer-reviewed studies that compared two or more monitoring. This provided an insight into the quantitative differences that are expected among methods. Next, we developed a forward-time, agent-based model to compare different approaches for collecting wildlife data, including data needed to quantify population size or density or understand disease presence. We found that the stationary approaches have more detections or sampling events compared to the mobile approaches, but both approaches have equal number of unique detections (i.e. unique individuals detected or sampled). Our model also suggested that some sampling approaches may be better suited for very large populations compared to other sampling schemes. Finally, we reviewed our current understanding of avian malaria with particular focus on recent advances in understanding this disease and its effects on wildlife and future efforts to control further spread. The results of this thesis will allow conservation practitioners and managers to identify the most effective monitoring techniques for their species of interest, and consideration of more efficient sampling approaches for wildlife diseases.